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ICML
2002
IEEE
14 years 10 months ago
Is Combining Classifiers Better than Selecting the Best One
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Saso Dzeroski, Bernard Zenko
ECML
2005
Springer
14 years 2 months ago
Error-Sensitive Grading for Model Combination
Abstract. Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of cla...
Surendra K. Singhi, Huan Liu
CVPR
2012
IEEE
11 years 11 months ago
Classifying covert photographs
The advances in image acquisition techniques make recording images never easier and brings a great convenience to our daily life. It raises at the same time the issue of privacy p...
Haitao Lang, Haibin Ling
ACL
1998
13 years 10 months ago
Classifier Combination for Improved Lexical Disambiguation
One of the most exciting recent directions in machine learning is the discovery that the combination of multiple classifiers often results in significantly better performance than...
Eric Brill, Jun Wu
EPIA
2009
Springer
14 years 3 months ago
Classifying Documents According to Locational Relevance
This paper presents an approach for categorizing documents according to their implicit locational relevance. We report a thorough evaluation of several classifiers designed for th...
Ivo Anastácio, Bruno Martins, Pável ...